Aggregate location dynometer (ALD)

ABSTRACT

An Aggregate Location Dynometer (ALD) in a physical wireless network alerts to a problematic crowd risk using location based services (LBS). An Aggregate Location Dynometer (ALD) comprises a Network Monitor, a Crowd Risk Determinant and an Alert Module. The Network Monitor monitors wireless traffic for a potential viral event, associated with a formation of a plurality of wireless devices. The Crowd Risk Determinant requests location information associated with a plurality of wireless devices in a given area regarding a respective viral event. The Crowd Risk Determinant determines if the viral event also indicates a crowd safety risk, based on the shape and movement of observed wireless devices. The Alert Module triggers an alert of an impending crowd problem when crowd risk is above a given threshold. Historical databases are empirically determined and maintained in the Aggregate Location Dynometer (ALD) for use in viral event and crowd risk assessment.

The present application is a continuation of U.S. application Ser. No.13/317,996 entitled “Aggregate Location Dynometer (ALD)”, filed on Nov.2, 2011, now U.S. Pat. No. 8,649,806; which claims priority from U.S.Provisional Application No. 61/573,112, entitled “Aggregate LocationDynometer (ALD)”, filed Sep. 2, 2011, the entirety of both of which areexpressly incorporated herein by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates generally to wireless telecommunications. Moreparticularly, it relates to cell location services, cell networktrafficking and analysis of location information.

2. Background of Related Art

Location based applications obtain a geographic position of a particularwireless device and provide services accordingly. Location basedservices (LBS) prevail in today's market due to an incorporation oftracking technology in handheld devices.

Location based pull services allow a wireless device user to locateanother wireless device. Current location services are generally focusedon individual wireless device user applications.

SUMMARY OF THE INVENTION

In accordance with the principles of the present invention, a method ofalerting to a problematic crowd risk in a given geographical location,comprises an Aggregate Location Dynometer (ALD). The Aggregate LocationDynometer (ALD) utilizes location based services (LBS) to analyzeaggregate location information pertaining to a multitude of wirelessdevices, to detect potential crowd risks.

An Aggregate Location Dynometer (ALD) resides in a physical networkserver, in accordance with the present invention, and comprises threemain components: a Network Monitor, a Crowd Risk Determinant, and anAlert Module.

The Network Monitor monitors a wireless network for indication of apossible impending viral event, in accordance with the principles of thepresent invention. In particular, the Network Monitor utilizes locationbased services (LBS) to monitor the formation of a plurality of wirelessdevices at a given point in a wireless network, e.g., a given basestation (BS). The Network Monitor compares obtained traffic parameterspertaining to monitored wireless traffic, with historical trafficparameters having to do with crowd risk determination, to determine if aviral event may be occurring or impending. A snapshot look at currentlocation data collected by the Network Monitor is subsequently logged inan appropriate historical database.

In accordance with the principles of the present invention, the CrowdRisk Determinant analyzes location information to determine if a viralevent triggered by the Network Monitor, also indicates a crowd safetyrisk. In particular, the Crowd Risk Determinant initiates a locationrequest to obtain location information pertaining to a multitude ofwireless devices in a given area, regarding a viral event that has beentriggered by the Network Monitor. The Crowd Risk Determinant comparesthe viral pattern formed by the shape and movement of wireless devicesin locations observed, with predetermined risk rules to determine if theviral event is also a crowd safety risk. The observed viral pattern issubsequently logged in an appropriate historical database.

The Alert Module, in accordance with the principles of the presentinvention, alerts proper authorities in an event of a crowd safety risk.The Crowd Risk Determinant triggers the Alert Module to alert of animpending crowd problem when crowd risk has exceeded a given threshold.

The Aggregate Location Dynometer (ALD) utilizes historical databases, inaccordance with the present invention, to maintain location-basedinformation indicating possible viral events associated with a pluralityof wireless devices. Historical databases include anAcceptable/Non-Acceptable Crowd Shape database, a Configurable ParameterThreshold database, a Historical Wireless Device Location Trendsdatabase, and a Risk Rules database.

BRIEF DESCRIPTION OF THE DRAWINGS

Features and advantages of the present invention will become apparent tothose skilled in the art from the following description with referenceto the drawings, in which:

FIG. 1 depicts an exemplary Aggregate Location Dynometer (ALD), inaccordance with the principles of the present invention.

FIG. 2 depicts the flow of an exemplary Network Monitor of the AggregateLocation Dynometer (ALD), in accordance with the principles of thepresent invention.

FIG. 3 depicts the flow of an exemplary Crowd Risk Determinant of theAggregate Location Dynometer (ALD), in accordance with the principles ofthe present invention.

FIG. 4 depicts the flow of an exemplary Alert Module of the AggregateLocation Dynometer (ALD), in accordance with the principles of thepresent invention.

FIG. 5 denotes first exemplary Aggregate Location Dynometer (ALD)location results, in accordance with the principles of the presentinvention.

FIG. 6 denotes second exemplary Aggregate Location Dynometer (ALD)location results, in accordance with the principles of the presentinvention.

FIG. 7 denotes third exemplary Aggregate Location Dynometer (ALD)location results, in accordance with the principles of the presentinvention.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

Thus far, location capabilities have been concerned with locating anindividual wireless device. Yet, there is such a vast abundance ofindividuals populating the nation's major cities. The present inventorhas appreciated the benefits of using location based services (LBS) toobtain sets of aggregate location data corresponding to a number andpattern of wireless devices within an area, region, city, etc. ofinterest.

The present invention introduces an Aggregate Location Dynometer (ALD),an analytical server utilizing location based services (LBS) on anetwork to predict public safety risks, e.g., the unexpected impendingformation of a flash mob, or a riot, etc.

The Aggregate Location Dynometer (ALD) analyzes a bird's-eye view ofpeople formation, presuming those individuals possess respectivehandheld wireless devices that permit collection of current locationinformation, whether that current location information be obtained fromthe wireless devices themselves, and/or from a network-based locationserver.

In accordance with the principles of the present invention, theAggregate Location Dynometer (ALD) predicts public safety risk in agiven geographical area through evaluation of the positioning andmovement of wireless devices. The Aggregate Location Dynometer (ALD)monitors wireless device network traffic to predict an impending viralevent. If a possible impending viral event is sensed from a generalmonitoring of wireless traffic, the Aggregate Location Dynometer (ALD)may request impending viral location information pertaining to clustersof wireless devices in a vicinity of the possible event, to moreaccurately assess crowd risk.

Crowd risk is assessed based upon given wireless network trafficparameters such as the number of wireless devices in communication witha given base station (e.g., a density), the shape formed byrepresentations of the individual locations of the densest areas whereactive wireless devices are currently located, and/or the movement ofthe wireless devices within the region as defined.

Markers, each representing a wireless device at a given location at agiven time, may be displayed on a display of the Aggregate LocationDynometer (ALD). The markers may represent wireless devices servedwithin the given region, whether actively communicating with anotherwireless device, or merely sensed as present.

The present invention preferably provides an alert of a possibleimpending crowd related public safety risk in real time, as the crowdrisk arises, informing emergency personnel as early as possible, evenbefore such event is consummated.

FIG. 1 depicts an exemplary Aggregate Location Dynometer (ALD) 400, inaccordance with the principles of the present invention.

In particular, an Aggregate Location Dynometer (ALD) 400 determinescrowd safety risk with the help of location based services (LBS) 318, asdepicted in FIG. 1.

The Aggregate Location Dynometer (ALD) 400 is generally based in aserver in a wireless network 322. Three main components form theAggregate Location Dynometer (ALD) 400: a Network Monitor 302, a CrowdRisk Determinant 304, and an Alert Module 306.

The Network Monitor 302 begins the risk determination process of theAggregate Location Dynometer (ALD) 400 by monitoring the network forindication of a possible viral event, in accordance with the principlesof the present invention. Determination of a viral event is the firststep in the escalation-based response of the Aggregate LocationDynometer (ALD) 400.

The Crowd Risk Determinant 304 assesses location information pertainingto a possible viral event triggered by the Network Monitor 302. TheCrowd Risk Determinant 304 determines if a viral event also indicates apublic safety risk.

The Alert Module 306 performs predetermined responsive measures to alertappropriate public safety personnel 320 in the event of a possible orprobable or current public safety risk.

Historical databases are empirically determined and maintained in theAggregate Location Dynometer (ALD) 400 for use in crowd risk assessment.The historical databases preferably store sets of aggregate currentlocation information pertaining to trackable wireless devices. Exemplaryhistorical databases accessible by the Aggregate Location Dynometer(ALD) 400 include but are not limited to a Historical Wireless DeviceLocation Trends and Statistics database 312, a Configurable ParameterThreshold database 310, a Risk Rules database 314, and anAcceptable/Non-Acceptable Crowd Shape database 308.

The Historical Wireless Device Location Trends and Statistics database312, as shown in FIG. 1, preferably stores sets of instantaneousaggregate location information obtained over a period of time. Datastored in the Historical Wireless Device Location Trends and Statisticsdatabase 312 provides empirical evaluation of crowd activities used todetect a crowd trend. The Aggregate Location Dynometer (ALD) 400preferably uses data stored in the Historical Wireless Device LocationTrends and Statistics database 312 to determine if a current situationis considered to be ‘normal’ to the monitored area, or abnormal,triggering a viral event. The data maintained in the Historical WirelessDevice Location Trends and Statistics database 312 is preferablyrefreshed over time.

The Configurable Parameter Threshold database 310, as depicted in FIG.1, preferably comprises a set of configurable location-based parametersand thresholds including density, clustering, spread, geographicalboundary, motion trends, and/or special events occurring in particularareas. The Configurable Parameter Threshold database 310 can alsoinclude non-location based parameters such as time of day and/or messagecontent. The parameters stored in the Configurable Parameter Thresholddatabase 310 are accessed by the Network Monitor 302 to assist indetecting a viral event.

The Risk Rules database 314, as shown in FIG. 1, preferably comprises aset of configurable location-based parameters and thresholds includingdensity, clustering, spread, geographical boundary, motion trends,and/or special events occurring in particular areas. The Risk Rulesdatabase 314 can also include non-location based parameters such as timeof day and/or message content. The parameters stored in the Risk Rulesdatabase 314 are accessed by the Crowd Risk Determinant 304 to assist indetermining if a viral event also indicates a public safety risk.

The Acceptable/Non-Acceptable Crowd Shape database 308, as shown in FIG.1, holds empirically determined past, historical cluster informationregarding acceptable and/or non-acceptable past shape formations ofclustered wireless devices. Specific shape parameters stored in theAcceptable/Non-Acceptable Crowd Shape database 308 are accessed by theCrowd Risk Determinant 304 to assist in determining if a viral eventalso indicates a public safety risk.

A viral event is the first state of alarm in the multi-state riskdetermination process of the Aggregate Location Dynometer (ALD) 400. Aviral event is defined as occurring when one or more predefinedparameter thresholds have been surpassed, as determined in the exemplaryembodiment in the Network Monitor 302. The occurrence of a viral eventdoes not necessarily infer a definite public safety risk. Instead, aviral event triggers the Crowd Risk Determinant 304 to further analyze apotentially malignant event more closely. For example, the Crowd RiskDeterminant 304 provides a closer inspection of aggregate currentlocation information, e.g., via use of a location-based push/pullservice. A match of more detailed location information to a historicalpattern leading to crowd risk may determine that a particular viralevent also indicates a likely public safety risk.

A public safety risk confirms a compromise in crowd safety, e.g., theimpending formation of a flash mob, or a riot, etc. Determination of apublic safety risk triggers the Alert Module 306 to implement properpublic safety response services.

The Network Monitor 302 begins the risk determination process of theAggregate Location Dynometer (ALD) 400, by monitoring the network forindication of a possible viral event, in accordance with the principlesof the present invention.

Moreover, the Network Monitor 302 retrieves subsequent sets ofinstantaneous aggregate location information. Location informationtriggered by the Network Monitor 302 may be portrayed in the form ofsnapshots displayed on a display of the Aggregate Location Dynometer(ALD) 400. Snapshots by the Network Monitor 302 comprise markers, eachrepresenting the location of individual wireless devices within a givenregion being monitored.

The Network Monitor 302 preferably obtains information regarding thenumber of wireless devices in a geographical area, at a given time,supported by a particular wireless network carrier (e.g., the number ofwireless devices sending messages over a wireless network via aparticular base station (BS) 324). The Network Monitor 302 usespredefined parameters and thresholds to determine if the monitorednetwork indicates that a viral event may be occurring or impending(e.g., surpassed parameter thresholds possibly indicative of anexcessive number and/or use of wireless devices for a given area, celltower, etc.).

For instance, a Maximum Number of Devices parameter may indicate themaximum number of wireless devices that may be present within range of aparticular base station (BS) 324 at a given time before a possible viralevent is triggered. The Maximum Number of Devices parameter may be setmanually, or empirically determined (e.g., the average number of devicespresent at a particular base station (BS) 324 over a course of time, asdetermined by historical data stored in the Historical Wireless DeviceLocation Trends and Statistics database 312).

The Network Monitor 302 triggers a possible viral event if a predefinedparameter threshold has been surpassed (e.g., a given density of currentlocation markers each representing a separate wireless device, or adirected convergence of at least two highly dense clusters of markerstoward each other at a significant rate of speed is or has occurred,etc.).

The Network Monitor 302 preferably tallies the number of wirelessdevices in each instantaneous aggregate location snapshot that iscaptured. Predetermined parameters and thresholds are used to assess thenumber (e.g., the density) of wireless devices in a particular area todetermine whether or not a possible viral event is occurring.

The Maximum Number of Devices parameter may alternatively be set toindicate the maximum number of wireless devices that may be present inan instantaneous aggregate location snapshot before a possible viralevent is triggered. If the number of devices present in a given snapshotexceeds the Maximum Value of Devices parameter established for therespective location, a viral event may be triggered.

The Network Monitor 302 also preferably tallies the difference in thenumber of wireless devices in a given area, from one consecutiveinstantaneous aggregate location snapshot to the next. If the differencein the number of wireless devices from snapshot to snapshot exceeds apredefined value in a number of consecutive snapshots for a given area,base station, etc., then a viral event may be triggered. Thresholds forsuch a predefined Maximum Difference in Number of Wireless Devicesparameter and a predefined Interval of Consecutive Snapshots parametermay be set manually, or empirically determined (e.g., the averagedifference in number of devices in consecutive instantaneous aggregatelocation snapshots capturing a particular area, e.g., a number of squarefeet, a particular base station (BS), etc., over a course of time,supported by a particular network carrier, as recorded in the HistoricalWireless Device Location Trends and Statistics database 312).

FIG. 2 depicts the flow of an exemplary Network Monitor 302 of theAggregate Location Dynometer (ALD) 400, in accordance with theprinciples of the present invention.

In particular, as shown in step 500 of FIG. 2, the Network Monitor 302preferably continuously, or at least periodically or intermittently,monitors network traffic.

In step 510, monitored wireless data traffic is inspected for thepresence of abnormal events, e.g., excessive volume for the time of day,etc. Configurable thresholds for the monitored parameters may be dynamicover the course of the day and even for traffic for any given tower orbase station. The configurable thresholds for monitored parameters maybe stored in the Configurable Parameter Threshold database 310.

As shown in step 520, if one or more parameter thresholds are exceeded,a viral event may be triggered. In response, the Network Monitor 302triggers the Crowd Risk Determinant 304 to perform a location-basedpush/pull service to determine the location of each trackable wirelessdevice within a particular geographic area (e.g., communicating throughgiven base stations or antennas).

When parameter thresholds are not surpassed, indicating that a viralevent is not occurring, location data may be logged in the HistoricalWireless Device Location Trends and Statistics database 312, as depictedin step 530. Location data logged in the Historical Wireless DeviceLocation Trends and Statistics database 312 may be used by the CrowdRisk Determinant 304 for future analyses of crowd risk.

FIG. 3 depicts the flow of an exemplary Crowd Risk Determinant 304 ofthe Aggregate Location Dynometer (ALD) 400, in accordance with theprinciples of the present invention.

In particular, the Crowd Risk Determinant 304 performs a location-basedpush/pull service to obtain location information pertaining to trackablewireless devices in a given area regarding a respective viral eventtriggered by the Network Monitor 302, as shown in step 540 of FIG. 3.

In step 550, collected location data is analyzed to assess the viralevent that is occurring. The Crowd Risk Determinant 304 uses bounds andpriorities set forth in the Risk Rules database 314 to determine if apossible viral event indicates a public safety risk. A viral pattern mayor may not imply public safety risk. In step 560, if a public safetyrisk is determined, the Crowd Risk Determinant 304 triggers the AlertModule 306 to take responsive public safety measures. Location dataassociated with a public safety risk is logged 530 in the HistoricalWireless Device Location Trends and Statistics database 312.

If the Crowd Risk Determinant 304 confirms that a particular viral eventdoes not indicate a public safety risk, the Aggregate Location Dynometer(ALD) 400 is triggered to routinely log location data 530 in theHistorical Wireless Device Location Trends and Statistics database 312for potential future analyses.

Determination of a public safety risk in the Crowd Risk Determinant 304triggers the Alert Module 306 to implement proper public safety responseservices. An Alert Module 306 is the final step in the riskdetermination process of the Aggregate Location Dynometer (ALD) 400.

FIG. 4 depicts the flow of an exemplary Alert Module 306 of theAggregate Location Dynometer (ALD) 400, in accordance with theprinciples of the present invention.

In particular, as shown in step 700 of FIG. 4, the Alert Module 306 istriggered by the Crowd Risk Determinant 304 and supplied thepredetermined conditions constituting how to handle a determined publicsafety risk.

The Alert Module 306 immediately alerts the proper authorities 320 inthe presence of a public safety risk, as depicted in step 710.

Subsequent aggregate data collections may be made by the Alert Module306 in step 720. A particular public safety event may be programmed toresult in multiple aggregate location data collections, set to occur atspecific intervals. Moreover, a particular risk determination result maybe configured to act as a triggered push/pull service 540 to acquireadditional location data. Subsequent location information is routinelylogged in the Historical Wireless Devices Location Trends and Statisticsdatabase 530.

Configurable parameters are maintained in the Risk Rules database 314 toassist the Crowd Risk Determinant 304 in determining if locationinformation pertaining to a viral event indicates a likely public safetyrisk. Factors for risk determination include but are not limited to theshape a cluster of location markers representing individual wirelessdevices of given density is forming, whether or not markers arespreading out or coming together, and/or at what rate of change acluster of wireless devices is moving. Factors for risk determinationalso include the behavior of collective XY location coordinates of themost dense clusters of wireless devices, to where the most denseclusters of wireless devices of concern are moving, and/or whether ornot a cluster of wireless devices in a particular location makes sensegiven the time of day.

For instance, empirical data may indicate that it is unusual for thereto be a large number of wireless devices present downtown after businesshours, or after a time when local bars and clubs have closed for thenight. In this case, a configurable threshold may be set for acombination of location and time of day parameters (e.g., to articulatethe number of wireless devices that must be present within a defineddowntown region, after a given hour) to trigger a public safety risk. Aconfigurable parameter threshold (e.g., specifying the number ofwireless devices capable of inhabiting a particular geographic expanseor particular shape of device formation, or a given density within thatregion) may manually or empirically be set. If a parameter threshold issurpassed, the Crowd Risk Determinant 304 informs the Alert Module 306of the development of a public safety risk.

The shape of a cluster of wireless devices may often offer significantclues to crowd risk potential. When location information is collected,the best-fit shape of dense clusters formed by accumulation of wirelessdevices in a given area may be determined. The best-fit shape of acluster of wireless devices may be compared against data contained inthe historical Acceptable/Non-Acceptable Crowd Shape database 308 todetermine danger potential. Different thresholds may be set for likeparameters based on varying location.

FIG. 5 denotes first exemplary Aggregate Location Dynometer (ALD) 400location results, in accordance with the principles of the presentinvention.

In particular, the large oval shape 101 formed by markers representingindividual wireless devices in the given geographical area 200 shown inFIG. 5, may be interpreted as a group of individuals enjoying a sportingevent in a stadium. Factors to consider are time of day and scheduledevents. The example in FIG. 5 uses precise location.

FIG. 6 denotes second exemplary Aggregate Location Dynometer (ALD) 400location results, in accordance with the principles of the presentinvention.

In particular, the pattern 102 in the geographical area 200 shown inFIG. 6 may be interpreted as cell sites pertaining to trackableindividuals, assuming most individuals carry wireless devices. The samepattern may mean different things at different hours of the day. Theexemplary location result shown in FIG. 6 uses coarse location.

FIG. 7 denotes third exemplary Aggregate Location Dynometer (ALD) 400location results, in accordance with the principles of the presentinvention.

In particular, the crescent shape 103 in the geographical area 200 shownin FIG. 7 is recognized as a pattern to be wary of. This crescent shapemay represent a variety of different occurrences (e.g., a protest infront of a given location such as a court house, a famous author at abookstore, etc.). The exemplary location result shown in FIG. 7 usesprecise location.

A rate-based parameter threshold may also or alternatively be set todefine an acceptable rate at which wireless devices would otherwisenormally inhabit a geographic area. For instance, if over a certainnumber of wireless devices enter an area in under a given amount of time(e.g., if three hundred wireless devices rush into a central pre-definedlocation in under ten minutes) then a public safety risk may betriggered.

Message content may be analyzed as an attribute for risk determinationin response to a viral traffic event. For instance, a determination ofthe most frequent phrases may be matched against a database of suspectedterms (e.g., “meet at the Lincoln Memorial”, etc.).

Motion trends are also analyzed to assess crowd risk. The Crowd RiskDeterminant 304 preferably determines whether the accumulation ofwireless devices is becoming more or less dense about a central locationand whether or not this behavior is expected based on trends andconfigured thresholds established for particular locations.

Precise accuracy of each individual device location is not extremelyimportant in the present invention. Instead, focus lies in the volume,density, shape and movement of data points collected. Serving cell towerlocations for each wireless device may be sufficient to satisfy initialtriggering requirements for a possible viral event. The AggregateLocation Dynometer (ALD) 400 is concerned with aggregate location dataas opposed to data involving individual device locations. Data regardingparameters such as special events, geographical boundaries, motiontrends, density, clustering, spread, time of day and/or message contentrelating to trackable wireless devices are recorded in the HistoricalWireless Device Location Trends and Statistics database 312, as opposedto exact locations of specific wireless devices. Anonymity regardingprecise locations of specific wireless devices alleviates some concernsurrounding the privacy of individuals during location based services(LBS), as used within the present invention.

An Aggregate Location Dynometer (ALD) 400 has benefit to entities otherthan emergency management and crowd risk assessment parties. Forinstance, the present invention may also be used to estimate locationtrends in cities, to rank areas such as parks and beaches by volume ofvisitors, and even to peg traffic patterns. Historical crowd data neednot represent a public safety issue, e.g., it may merely relate to cityplanning or disaster recovery. Thus, data collected while scanning forcrowd risk provides cities, states and government with valuableinformation.

Though, preferably all wireless devices in a given area would bemonitored for crowd gathering tendencies, it is also within theprinciples of the present invention to monitor only those devices by therelevant wireless carrier providing Location Dynometer (ALD) 400services.

The present invention greatly benefits police, fire and generalemergency response personnel 320 desiring early warning about possiblecrowd related risks, e.g., riots. Moreover, the present invention isintended to combat nefarious cell technology to spawn mobs and riotswithout resorting to network restrictions.

While the invention makes use of the current location data of preferablyall wireless devices within a given region, area, etc., the inventionalso preferably makes distinction between the current mode of operationof the wireless devices being analyzed for a possible public safetyrisk. For instance, analysis of the density, shape, movement, etc. indetermining a possible public safety risk may analyze only wirelessdevices in active mode.

While the invention has been described with reference to the exemplaryembodiments thereof, those skilled in the art will be able to makevarious modifications to the described embodiments of the inventionwithout departing from the true spirit and scope of the invention.

What is claimed is:
 1. An aggregate location dynometer in a physicalwireless network server, said aggregate location dynometer comprising: anetwork monitor to monitor a wireless network for an indication of aviral event; a location aggregator to obtain a location of each of aplurality of wireless devices associated with said viral event; a crowdrisk determinant, triggered by said network monitor, to determine acrowd risk based on an aggregation of said location of each of saidplurality of wireless devices associated with said viral event; and analert module to initiate an alert message relating to a public safetyrisk determined from an analysis of said viral event.
 2. The aggregatelocation dynometer in a physical wireless network server, said aggregatelocation dynometer according to claim 1, further comprising: ahistorical database maintaining a geographic region associated with saidviral event.
 3. The aggregate location dynometer in a physical wirelessnetwork server, said aggregate location dynometer according to claim 2,wherein said historical databases comprises: a plurality of acceptablecrowd shapes, a crowd shape being defined by said aggregation of saidlocation of each of said plurality of wireless devices associated withsaid viral event.
 4. The aggregate location dynometer in a physicalwireless network server, said aggregate location dynometer according toclaim 2, wherein said historical databases comprises: a plurality ofnon-acceptable crowd shapes, a crowd shape being defined by saidaggregation of said location of each of said plurality of wirelessdevices associated with said viral event.
 5. The aggregate locationdynometer in a physical wireless network server, said aggregate locationdynometer according to claim 2, wherein said historical databasescomprises: a configurable parameter defining a threshold of a crowdshape becoming unacceptable and thus said crowd risk.
 6. The aggregatelocation dynometer in a physical wireless network server, said aggregatelocation dynometer according to claim 2, wherein said historicaldatabases comprises: a plurality of crowd shape trends based onhistorical wireless device locations during previous viral events.
 7. Amethod of alerting to a problematic crowd risk based on location basedservices (LBS), comprising: monitoring wireless traffic for a formingviral event associated with a plurality of physical wireless deviceswithin a given region; initiating location requests to a physicallocation server to obtain a current location of each of said pluralityof physical wireless devices; forming a crowd shape based on anaggregation of said current location of each of said plurality ofphysical wireless devices; determining a crowd risk of said crowd basedon said crowd shape of said current location of each of said pluralityof physical wireless devices; and triggering a crowd alert message whensaid determined crowd risk is above a given threshold.
 8. The method ofalerting to a problematic crowd risk with location based services (LBS)according to claim 7, wherein: said crowd risk of said crowd is furtherdetermined based on a movement of said crowd shape.
 9. The method ofalerting to a problematic crowd risk with location based services (LBS)according to claim 7, wherein said monitoring wireless trafficcomprises: monitoring wireless traffic at a given point in a wirelessnetwork; and comparing a given traffic parameter associated with saidobtained current location of each of said plurality of physical wirelessdevices, with a historical traffic parameter associated with a previousproblematic crowd formation.
 10. The method of alerting to a problematiccrowd risk with location based services (LBS) according to claim 9,wherein: said given point is at a given base station in said wirelessnetwork.
 11. The method of alerting to a problematic crowd risk withlocation based services (LBS) according to claim 9, further comprising:logging a snapshot formation created by said current location of each ofsaid plurality of physical wireless devices.
 12. The method of alertingto a problematic crowd risk with location based services (LBS) accordingto claim 7, wherein said initiating location requests comprises:initiating a location request for each of said plurality of physicalwireless devices.
 13. The method of alerting to a problematic crowd riskwith location based services (LBS) according to claim 7, furthercomprising: comparing a viral pattern of respective locations of saidplurality of wireless devices to predetermined risk rules.
 14. Themethod of alerting to a problematic crowd risk with location basedservices (LBS) according to claim 13, further comprising: logging saidviral pattern.